ice nucleation by cellulose and its potential ice ... · 1 1 supplementary information on “ice...

1

Supplementary information on “Ice nucleation by cellulose and its potential contribution to 1

ice formation in clouds” 2

3

N. Hiranuma1*, O. Möhler1, K. Yamashita2,3, T. Tajiri2, A. Saito2, A. Kiselev1, N. Hoffmann1, 4

C. Hoose1,4, E. Jantsch5, T. Koop5 & M. Murakami2. 5

6

January 16, 2015 7

8 1Institute for Meteorology and Climate Research – Atmospheric Aerosol Research, Karlsruhe 9

Institute of Technology, Karlsruhe, Germany. 10 2Meteorological Research Institute, Tsukuba, Japan. 11 3Snow and Ice Research Center, National Research Institute for Earth Science and Disaster 12

Prevention, Nagaoka, Japan. 13 4Institute for Meteorology and Climate Research – Troposphere Research, Karlsruhe Institute of 14

Technology, Karlsruhe, Germany. 15 5Faculty of Chemistry, Bielefeld University, Bielefeld, Germany. 16

17

*Corresponding Author. E-mail: [email protected] 18

19

20

Table of Contents: 21

1. Supplementary discussion 22

1.1. Aerosol characterization 23

1.2. MCC particle size distributions 24

1.3. Examination of contaminations 25

1.4. Additional MRI-DCECC measurements 26

1.5. Additional model-based estimate 27

1.6. Comparison of ice nucleating properties of different cellulose types 28

2. Supplementary method 29

3. Supplementary tables 30

Ice nucleation by cellulose and its potential contribution to ice formation in clouds

SUPPLEMENTARY INFORMATIONDOI: 10.1038/NGEO2374

NATURE GEOSCIENCE | www.nature.com/naturegeoscience 1

© 2015 Macmillan Publishers Limited. All rights reserved

http://dx.doi.org/10.1038/ngeo2374

2

1. Supplementary discussion 31

32

1.1. Aerosol characterization 33

34

BET: The specific surface area (SSA in surface area per unit mass) of bulk 35

microcrystalline cellulose (MCC; Aldrich, 435236) and fibrous cellulose (FC; Sigma, C6288) 36

was measured by using the Brunauer, Emmett and Teller (BET; Brunauer et al.31

) N2-adsorption 37

technique (Gregg and Sing.32

). A relatively low SSA of MCC (1.44 ± 0.1 m2 g

-1 and density ~ 38

1.5 g cm-3

) and FC (1.31 ± 0.1 m2 g

-1) was found when compared to granulated illite-rich mineral 39

particles (SSA = 104.2 ± 0.7 m2 g

-1 and density = 2.65 g cm

-3; Broadley et al.

33) and kaolinite-40

rich mineral particles (SSA = 11.8 ± 0.8 m2 g

-1 and density = 2.6 g cm

-3; Murray et al.

34). 41

HIM: To complement the bulk BET-SSA measurement, helium ion microscopy (HIM; 42

e.g., Joens et al.35

) was performed to characterize the nanoscale surface structure of MCC on a 43

single particle basis. Representative HIM images of MCC are shown in Fig. S1. As can be 44

inferred from the figure, MCC has a complex porous morphology or capillary spaces between 45

the nanoscale fibrils over the microfiber surface. In addition to MCC characterization, HIM 46

analysis was also performed on FC to characterize its nanoscale surface structure (not shown). 47

We observed that FC also had similar surface morphology compared to MCC. These surface 48

structures may make cellulose accessible to water and may even induce the capillary 49

condensation of water in these pores due to the inverse Kelvin effect (e.g., Christenson.36

; 50

Marcolli.28

). This supports the view that MCC may be a good proxy for inferring water uptake, 51

wettability and ice nucleating properties of various cellulose materials. 52

XRD: The structural lattice parameters of MCC and FC were measured using the X-ray 53

diffraction (XRD; Waseda et al.37

) technique and compared to the crystallographic properties of 54

general monoclinic cellulose (i.e., reference number 00-003-0289 and 00-050-2241 from the 55

International Centre for Diffraction Data). The XRD results suggest that the structural properties 56

of both cellulose materials are in agreement (i.e., P21 space group; a = 7.96 Å, b = 8.35 Å, c = 57

10.28 Å) and comparable to Cellulose Iβ (Nishiyama et al.22

). We note that the majority of MCC 58

has an ordered crystalline structure, but some fractions (up to 20%) are disordered according to 59

the X-ray scattering measurement (Quiroz-Castañeda and Folch-Mallol.21

; Fernandes et al.38

; 60

Šturcová et al.39

). This amorphous content is known to be more hydrolysable than the crystalline 61


3

one (Quiroz-Castañeda and Folch-Mallol.21

). Further quantification of influences of crystalline 62

and non-crystalline morphology on the immersion freezing ability of cellulose materials is an 63

important topic for future work. The XRD spectra of MCC and FC powder used in this study are 64

available upon request. 65

66

1.2. MCC particle size distributions 67

68

The total surface area and mass of MCC particles were obtained from measurements of a 69

scanning mobility particle sizer (SMPS; TSI Inc., Model 3081 differential mobility analyzer, 70

DMA, and Model 3010 condensation particle counter, CPC) and the welas optical particle 71

counter (welas-OPC; PALAS, Sensor series 2500). The standard SMPS system used in our 72

studies was not able to account for and adjust to the sampling pressure quickly changing during 73

the expansion experiments. Therefore, reliable and accurate SMPS size distribution 74

measurements were only possible prior to the start of the expansion at MRI-DCECC. To 75

minimize the sampling loss of MCC particles larger than 1 µm diameter and related biases, we 76

used the welas-OPC directly below the MRI-DCECC vessel with a vertically aligned inlet from 77

the chamber vessel and applied these data to estimate the total surface area and mass of coarse 78

MCC particles. Besides the less pronounced particle loss of coarse particles, another merit of the 79

SMPS-welas-OPC combination is its wide range of size coverage (up to ~46 μm volume 80

equivalent diameter, Dve). Hence, our approach to estimating the total surface correctly considers 81

both the number and size of particles. 82

As the welas-OPC measures optical scattering intensities from the particles which are 83

converted to actual particle sizes by the Mie theory assuming spherical particles of known 84

refractive index, this OPC cannot accurately measure sizes of non-spherical or irregularly 85

structured particles. This typically results in overestimations of their actual sizes compared to the 86

optical particle sizes (Dopt) by a factor of about two for non-spherical ice crystals (Wagner et 87

al.40

). We also expected and observed a similar degree of size overestimation in our studies. 88

Accordingly, simultaneous measurements of the same cellulose aerosol with an aerodynamic 89

particle sizer (APS; TSI Inc., Model 3321) and the welas-OPC were carried out at MRI-DCECC, 90

and a correction factor of ~0.45 was found to convert Dopt to Dve in our study. Because we do not 91

know the physical relationship between the welas optical diameter and the APS aerodynamic 92


4

diameter, we took the mean value of 0.45 from the 6 data points shown in Fig. S2 as an estimate 93

of this conversion factor. This conversion factor was applied to generate the corrected size 94

distribution presented in Fig. 1. The largest MCC particle observed by an optical particle counter 95

was ~24 μm in Dve. The obtained size distribution as a function of Dve was then directly 96

combined with the data from the SMPS for which the electrical mobility diameter (Dmo) is 97

equivalent to the volume equivalent diameter assuming all particles are spherical (Peters et al.41

). 98

This assumption is justified by the fact that the smaller cellulose particles in the SMPS size range 99

(<0.7 µm in diameter; see Fig. 1) appeared to have compact and close to spherical shapes (see 100

discussion below). 101

102

1.3. Examination of contaminations 103

104

To examine a potential contamination during aerosol generation, and to further validate 105

the APS and SMPS size distribution analysis of MCC, an additional characterization of particle 106

size distributions and associated compositions was performed using a ~4 m3 stainless steel vessel. 107

Dry ground MCC was injected directly into the chamber using the same rotating brush generator 108

(PALAS, RBG1000) as in our experiments with MRI-DCECC. When particle concentrations 109

reached 60 cm-3

, aerosol injection was stopped. Afterwards, the temperature and pressure in the 110

chamber were maintained at 21.8 ± 0.01˚C and 995.5 ± 0.03 hPa, respectively, until the end of the 111

aerosol measurements. Particle size distributions of MCC in the well-mixed chamber vessel were 112

directly measured using an SMPS and an APS. 113

Concurrent filter sampling, followed by off-line characterization of electron microscopy 114

images and energy dispersive X-ray (EDX) spectroscopy, was carried out to assess the 115

morphology, 2-D area equivalent diameter (Da), intensity of backscattered electrons and 116

compositions of the MCC particles. A total of 503 particles collected on either a 47 mm 117

Nuclepore® substrate (0.2 μm pore size filter; Whatman, 111106) or a copper microscopy 118

substrate was imaged by the scanning electron microscope (SEM; FEI, Quanta 650 FEG). As 119

shown in Fig. S3, the larger MCC particles have an elongated shape reflected by an average 120

aspect ratio of 2.1. Small particles (<0.7 µm Da) appeared to be more spherical than larger 121

particles, and the largest MCC particle on our filter observed by SEM was ~10 μm Da. More 122


5

importantly, the SEM-EDX results showed that more than 97% of the detected particles larger 123

than 0.5 μm in Da (a total of 601 particles) were pure cellulose without any contaminations. 124

The normalized surface area distributions (scaled to the total surface areas) over the range 125

from 0.01 to 16 μm in diameter (Dp) are presented in Fig. S4. The electrical mobility diameter 126

determined by the SMPS was converted into Dve assuming a shape factor of 1 which is justified 127

by the above mentioned observation that MCC particles smaller than about 1 µm had more 128

compact shapes than the larger ones. The best match between the APS and the SEM-derived size 129

distribution was achieved with a factor of about 0.8 for converting the APS aerodynamic 130

diameter into Dve for comparison with Da. This conversion factor is given by the square root of 131

the ratio of the dynamic shape factor and the particle density (Peters et al.41

). Taking the 132

literature value of 1.5 g cm-3

for the MCC density (Sun.42

) would thus imply a shape factor of 133

about 1 also for the larger MCC particles in the size range of the APS. However, the SEM 134

analysis showed these particles to be clearly non-spherical and therefore to have as shape factor 135

larger than one. This indicates the density of MCC particles examined in this study to have a 136

somewhat larger density than 1.5 g cm-3

. With these assumptions and corrections, the SMPS and 137

APS surface area distributions shown in Fig. S4 agree well with the SEM derived surface area 138

distribution except a few data points above 6 μm Dp. Surface area fractions for Da > 6 μm 139

estimated from SEM measurements are higher than those of the APS. This discrepancy may be 140

due to some loss of larger particles in the horizontal section of the APS sampling line or an 141

overestimation of the SEM result for larger particles due to a sampling artefact or the fact that 142

the SEM values for the largest particles are calculated from only a small number of particles. 143

Assuming a larger shape factor for particles larger than about 6 µm would also result in a better 144

agreement between the APS and SEM data. 145

It is noteworthy that the surface area distributions of MCC were dominated by 146

supermicron particles (mode diameter ~2.2 μm) with a negligible contribution of particles 147

smaller than 0.5 μm diameter to the total surface area (Fig. S4). Similar trends and reproducible 148

particle surface area distributions of <0.5 μm Dve were observed during the MRI-DCECC 149

experiments regardless of grinding and the use of cyclone impactors prior to injection. An 150

average (± standard error) of 8.7 (± 2.6) % of the total surface area was attributed to <0.5 μm Dve 151

MCC particles in fourteen experiments. Based on this analysis, the surface area distributions 152


6

below ~0.7 μm diameter for MRI03_140117b and MRI03_140116 with no SMPS available were 153

estimated from the average normalized particle size distributions presented in the inset of Fig. S4. 154

At MRI-DCECC, both the brush and the solid material reservoir of a rotating brush 155

generator were washed with distilled water and dried in a drying oven to prevent carryover of 156

sample residues into the next sample. Prior to each particle loading, aerosol-free dry synthetic air 157

was passed through the RBG for >30 minutes. We confirmed that the background aerosol 158

concentration was typically ~0.1 cm-3

. 159

160

1.4. Additional MRI-DCECC measurements 161

162

Figure S5 summarizes six DCECC experiments (alphabetically sorted panels a to f) 163

analyzed in this study. Temporal profiles of the chamber mean temperature and pressure are 164

presented in panels i and ii, respectively. Simultaneous reduction of the thermally insulated 165

vessel wall and gas temperatures was achieved by uniformly controlling the wall temperature 166

with refrigerated coolant and mechanically evacuating air in the chamber, respectively. All six 167

experiments were conducted by employing a constant cooling rate of -2.4 to -2.8 ˚C min-1

168

(equivalent to the updraft rate of 4.1 to 4.7 m s-1

) from the initial gas temperature of typically 169

about 5 ˚C (except for MRI02_131009b that started at -4.7 ˚C) until either homogeneous ice 170

nucleation (around -37 ˚C; Koop et al.43

) or sedimentation of heterogeneously formed ice crystals 171

was observed. An increase in backward scattering intensities parallel to the linear polarisation 172

state of the incident laser beam (Iback,par in panels iii) reflected droplet growth in the DCECC. An 173

increase in the depolarisation ratio indicated the formation and growth of ice crystals (Tajiri et 174

al.9). In addition, image acquisition of ice crystals by a cloud particle imager (CPI; SPEC, Inc.) 175

was also carried out for the detection of ice crystals (panel f. iv.). Ice crystal concentrations (Nice) 176

were quantified by summing particles larger than 30 μm Dve measured by the welas-OPC (panels 177

iv). One caveat of the DCECC experiments is the pressure change during the mechanical 178

expansion. The resulting concentration change related to the pressure change was accounted in 179

our ns calculation as described in the method section. 180

We attempted to investigate droplet-freezing properties of polysaccharose (Merck KGaA, 181

107687). The injection of polysaccharose particles into the chamber was carried out by the 182

atomization of its solution (1.0 weight % in 18.2 MΩ cm ultrapure water). Two experiments 183


7

(MRI02_131009b and MRI02_131011b; not shown) were conducted at a constant cooling rate of 184

about -3 ˚C min−1

from the initial gas temperature of about 5 ˚C until the start of homogeneous 185

ice nucleation of solution droplets. No heterogeneous ice nucleation was observed. These results 186

also demonstrate a negligible contribution of the chamber-background aerosol to ice formation. 187

The background aerosol concentration is typically ~0.1 cm-3

. 188

189

1.5. Additional model-based estimate 190

191

In an effort to reduce the uncertainty associated with carbonaceous aerosol radiative 192

forcing, the EU-funded CARBOSOL (a.k.a., Present and retrospective state of organic versus 193

inorganic aerosol over Europe: implications for climate) project was conducted from 2001 to 194

2005 (Legrand and Puxbaum.44

). From mid-2002 to mid-2004, aerosol samplings as well as 195

organic speciation studies were carried out at six selected ground sites in different environments 196

across Europe (Fig. S6). 197

We also applied the ice nucleation parameterization for cellulose to observed cellulose 198

and plant debris concentrations at each CARBOSOL site and compared the predicted ice 199

nucleating particle (INP) concentrations derived from desert dust (Fig. S7). The temperature-200

dependent fit to INP observations by DeMott et al.20

is included in Figs. 3 and S7 to indicate the 201

order of magnitude of atmospheric INP concentrations. However, the observations originate 202

from various sites around the world (e.g., the Arctic and the Amazon Basin) and from altitudes 203

between the boundary layer and the upper troposphere, such that it is impossible to make a one-204

to-one comparison between this fit and the simulated dust ice nucleation spectra. 205

206

1.6. Comparison of ice nucleating properties of different cellulose types 207

208

Here we describe experiments aimed at comparing ice nucleation properties of different 209

types of cellulose. For this purpose, droplet freezing experiments were performed using the 210

BINARY (Bielefeld Ice Nucleation ARaY) technique, which is described in detail in Budke and 211

Koop45

. Briefly, 36 droplets of 1 µL volume each are pipetted into a 6x6 array of individual 212

compartments, consisting of a siliconized glass slide on which the droplets are placed, a PDMA 213


8

polymer spacer that forms and separates the compartments, and a top glass slide to prevent mass 214

transport between the droplets from different compartments. This sample cell is placed onto a 215

Peltier cooling stage situated within a nitrogen-purged chamber (Linkam LTS 120 system). The 216

temperature of the cell is measured and the cooling rate of 1 °C min-1

is controlled by a 217

LabVIEW™ virtual instrument. This program is also used for the automated detection of ice 218

nucleation events from the change in individual droplet opacity during freezing as observed in 219

images obtained by a CCD camera (QImaging MicroPublisher 5.0 RTV). 220

Two types of high purity cellulose powders were investigated. MCC from the identical 221

batch used for the DCECC cloud simulation chamber experiments described in the main text and 222

in addition a FC sample. The cellulose particles were suspended in water in two different ways. 223

First suspensions were prepared by dispensing a predetermined mass of cellulose particles in bi-224

destilled water via thorough shaking by hand. This method produced rather instable suspensions 225

as a quickly changing opacity of the suspensions was observed within seconds to minutes 226

indicating sedimentation of cellulose particles. Therefore, another method was devised. 227

Dispersion of the cellulose in bi-destilled water was obtained using a high-speed rotor-stator 228

homogenizer (IKA Ultra-Turrax T-25, S25N-8G) for 5 min at 20,000 RpM. These suspensions 229

were observed to be more stable and, hence, are considered to produce more reliable results. 230

However, since the homogenization process is likely to change the specific surface area of the 231

cellulose by disruption of individual particles we report experimental ice nucleation data in terms 232

of the active site per dry mass of cellulose 𝑛𝑚(𝑇) here, instead of the 𝑛𝑠(𝑇) values reported in 233

the main text. Therefore, the 𝑛𝑚(𝑇) values presented here are not directly comparable to the 234

𝑛𝑠(𝑇) values from the DCECC cloud chamber. Nevertheless, the 𝑛𝑚(𝑇) values are well suited to 235

compare the ice nucleating properties of different types of cellulose. 236

𝑛𝑚(𝑇) was calculated by 𝑛𝑚(𝑇) = −ln (1 − 𝑓ice(𝑇)) (𝐶𝑚 𝑉drop)⁄ , where 𝑓ice(𝑇) is the 237

cumulative frozen fraction as a function of temperature obtained from a total of 108 droplets (3 x 238

36) for each cellulose mass concentration 𝐶𝑚 investigated with BINARY for droplets with a 239

volume 𝑉drop = 1 µL. For the hand shaken samples mass concentrations of 1 and 0.1 mg mL-1

240

were prepared and investigated and for the homogenized samples mass concentrations of 5, 1, 241

0.5 and 0.1 mg mL-1

were studied. For each sample and concentration freezing data were 242

obtained while cooling at 1 °C min-1

from 0 °C until all droplets were frozen. 243


9

The results obtained from the BINARY measurements are shown in Fig. S8. The solid 244

data points represent mean 𝑛𝑚(𝑇) values, obtained by averaging the data from the different 245

cellulose concentrations for which an ice nucleation event occurred within a particular 246

temperature interval of ∆T = 0.25 °C in the investigated temperature range below 0 °C. A 247

variation coefficient was obtained from the cumulative 𝑛𝑚(𝑇) value of all concentrations in each 248

temperature interval and averaged over all temperature intervals. The resulting medium variation 249

coefficient was used to calculate the relative error of 𝑛𝑚(𝑇) in each temperature interval 𝑖 from a 250

moving average including the neighboring temperature intervals 𝑖 + 1 and 𝑖 − 1. The shaded 251

areas in Fig. S8 represent this data variability in 𝑛𝑚(𝑇). 252

Figure S8 indicates that the two cellulose types, MCC (red) and FC (blue), show a similar 253

behavior of 𝑛𝑚 as a function of temperature for both the homogenized suspension samples (a) as 254

well as the hand shaken samples (b). In particular, the results of the homogenized MCC and FC 255

samples agree well over the entire temperature range with ice nucleation events occurring from 256

about –27 °C up to –10 °C. In contrast, the hand shaken samples did not show any freezing 257

events at temperatures higher than about –18 °C. This may have been due to the fact that these 258

suspensions suffered from sedimentation, as discussed above and indicated also by the high 259

variability of the MCC data at –18 °C to –22 °C. Because large particles are most likely to 260

precipitate thereby diminishing the available mass and surface area for ice nucleation, the 261

observed 𝑛𝑚(𝑇) values in Fig. S8b can be regarded as lower limits only. In addition, the 262

homogenization process presumably enlarges the specific surface area of the cellulose particles, 263

leading to higher 𝑛𝑚(𝑇) values in panel a when compared to those of panel b. Nevertheless, 264

even though the numerical values of the data shown in panel b are not reliable, they support that 265

the two cellulose types nucleate ice in supercooled water similarly or each sample preparation 266

method. We note that a limited number of measurements on suspensions prepared in an ultra 267

sound bath for 16 hours (𝐶𝑚 = 1 mg mL-1

, not shown) also show a similar behavior of 𝑛𝑚(𝑇) for 268

MCC and FC, corroborating the results presented in Fig. S8. We further note that our result are 269

in good agreement with previous findings by Muhr et al.46

who investigated ice nucleation 270

temperatures of supercooled aqueous sucrose bulk solutions (58.6 wt%) containing different 271

cellulose types (microcrystalline cellulose and various types of methyl cellulose and sodium 272

carboxy-methyl cellulose) at mass concentrations of 6.7 mg mL-1

. Within the experimental 273


10

uncertainty of ±2 °C, all cellulose types showed very similar cumulative numbers of ice crystals 274

as a function of temperature. 275

We conclude that the results presented in Fig. S8 suggest that there is no significant 276

difference of the immersion ice nucleation activity of microcrystalline and fibrous cellulose in 277

supercooled water, independent of the method of suspension preparation. 278


11

279

Figure S1. High resolution helium ion microscopy images of an individual MCC particle. A 280

2x magnified version of the left image (a) is the image on the right side (b). 281


12

282

Figure S2. Relationship between APS and welas-OPC. APS sizes (± 10% error) are plotted 283

against the welas-OPC sizes (± 4% error) for the MCC concentration range from 0.025 to 550 284

cm-3

. The dotted line represents the 1:1 line. Note that the aerodynamic diameter obtained from 285

the APS was converted into the volume equivalent diameter (Dve). 286


13

287

Figure S3. Electron microscopy images of elongated MCC particles. Dry MCC particles were 288

collected on a Nuclepore filter from the aerosol chamber. A 4x magnified version of the left 289

image (a) is the image on the right side (b). An average aspect ratio of 2.1 was estimated by the 290

ratio of the length along the major axis to that along the minor axis on a single particle basis for 291

503 MCC particles. 292


14

293

Figure S4. Surface area distributions of MCC particles. Particle size distributions were 294

measured by a combination of an SMPS (0.01 to 0.4 μm), an APS (0.4 to 16 μm) and the off-line 295

SEM analysis (as small as 0.3 μm). Black and red data points are the particle surface area 296

distributions normalized to the total surface area concentration. The x-axis error bar on a selected 297

SEM data point (at 0.85 μm Da) reflects the range of uncertainty in the particle size derived from 298

the average aspect ratio of MCC particles (i.e., 2.1 from an electron micrograph). The dashed 299

line on APS data points represents a lognormal fit for >0.5 μm Dve. The sub-panel shows the 300

average of fourteen surface area distributions of submicron MCC particles scaled to the total 301

SMPS surface areas, which were measured at MRI. Note that both axes are in the log scale. 302


15

303

Figure S5. Temporal plots of the DCECC freezing experiments. Alphabetical panels are 304

sorted based on experiment, including MRI03_140117b (a), MRI03_140116 (b), 305

MRI03_131218b (c), MRI03_131218a (d), MRI03_131219 (e) and MRI02_131009b (f). Arrays 306

of numerical panels represent chamber gas temperature (T) (i), pressure (P) (ii), backscattered 307

light scattering intensity parallel to the incident polarisation state (left axis of panels iii) and 308

depolarisation ratio (right axis of panels iii) and pressure-corrected number concentration of ice 309

crystals [Nice(P) = (P0/Pt) × Nice] (iv). to corresponds to the beginning of mechanical expansion. 310

The representative CPI images (~200 μm horizontal perimeters) of ice crystals obtained during 311

MRI02_131009b (551 s, 14:30:47 JST) are shown in panel f.iv. 312


16

313

314

Figure S6. CARBOSOL sampling sites across Europe. Sampling sites are located along an 315

East-West transect extending from the Azores to Hungary: Azores (AZO), Aveiro (AVE), Puy 316

de Dôme (PDD), Schauinsland (SIL), Sonnblick (SBO) and K-Puszta (KPZ). 317


17

318

Figure S7. Potential INP spectra at each CARBOSOL site. INP concentrations were 319

calculated from simulated desert dust concentrations and cellulose concentrations observed at the 320

six CARBOSOL sites (panels a to f). The dashed line represents a temperature-dependent fit on a 321

compiled dataset of estimated INP concentrations derived from the >0.5 μm diameter particles 322

observed (DeMott et al.20

). 323


18

324

Figure S8. Number of ice nucleation active sites per cellulose dry mass, 𝒏𝒎, as a function of 325

temperature T for microcrystalline and fibrous cellulose. Suspensions were prepared by 326

either homogenization (a) or via shaking by hand (b). The blue and red data points represent 327

average 𝑛𝑚-values from data of differently concentrated suspensions (Cm of 5, 1, 0.5 and 0.1 mg 328

mL-1

for homogenized samples in a and Cm of 1 and 0.1 mg mL-1

for hand shaken samples in b). 329

The shaded areas indicate the data variability, see text for more details. 330


19

2. Supplementary method 331

332

Model-based estimate: The simulations with CAM4-Oslo were carried out for five years, 333

of which the first year was considered to be the as model-spinup, at a resolution of 1.875°x2.5° 334

with 26 vertical levels. The uncertainty in the dust ice nucleation (shaded blue area) is estimated 335

to be at least a factor of 10 (e.g. Burrows et al.19

), while the minimum monthly mean cellulose 336

concentration and the maximum monthly mean plant debris concentration are taken as lower and 337

upper limits, respectively, for the cellulose ice nucleation indicated by the shaded green area. 338


20

3. Supplementary tables 339

340

The biannual average mass concentrations of airborne cellulose and plant debris as well 341

as estimated size-binned surface area of airborne cellulose are summarized in Table S1 and S2, 342

respectively. These data were adapted to CAM4-Oslo to generate Figs 3 and S7. 343


21

Table S1. Summary of the biannual average mass concentrations of airborne cellulose and plant 344

debris from CARBOSOL sites (Sánchez-Ochoa et al.7). 345

Site

Country

Latitude/

Longitude

Height, m

a.s.l.

Biannual average mass conc.,

ng m-3

Total

Cellulose Plant Debris

Azores (AZO) Portugal 38˚38'N/27˚02'W 50 16.3 33.4

Aveiro (AVE) Portugal 40˚34'N/8˚38'W 40 71 142

Puy de Dôme (PDD) France 45˚46'N/2˚57'E 1450 83 168

Schauinsland (SIL) Germany 47˚55'N/07˚54'E 1205 122 245

Sonnblick (SBO) Austria 47˚03'N/12˚57'E 3106 47 93.3

K-Puszta (KPZ) Hungary 46˚58'N/19˚35'E 136 181 363

346


22

Table S2. Summary of estimated size-binned surface area (in μm2 cm

-3) of airborne cellulose. 347

Surface area distributions were derived from the total mass of cellulose (avg & min) and plant 348

debris (max) given in Table 2 and 5 of Sánchez-Ochoa et al.7 adapted to the particle mass 349

distribution presented in Fig. 3 of Puxbaum and Tenze-Kunit8. Reported values in bracket 350

represent min/max range (i.e., Min/Max). 351

Dve, μm CARBOSOL sites

AZO AVE PDD

0.2 3.96E-02 1.73E-01 2.02E-01

(0.08/0.33) (1.22E-02/3.35E-01) (1.70E-02/1.44E+00) (9.72E-03/1.53E+00)

0.8 2.46E-02 1.07E-01 1.25E-01

(0.33/1.31) (7.54E-03/2.08E-01) (1.06E-02/8.91E-01) (6.03E-03/9.48E-01)

3.3 6.44E-03 2.80E-02 3.28E-02

(1.31/5.31) (1.97E-03/5.45E-02) (2.76E-03/2.33E-01) (1.58E-03/2.48E-01)

12.9 1.23E-03 5.34E-03 6.24E-03

(5.31/20.41) (3.76E-04/1.04E-02) (5.26E-04/4.44E-02) (3.01E-04/4.73E-02)

Dve, μm CARBOSOL sites

SIL SBO KPZ

0.2 2.96E-01 1.14E-01 4.40E-01

(0.08/0.33) (1.70E-02/2.49E+00) (1.46E-02/1.12E+00) (6.80E-02/2.97E+00)

0.8 1.84E-01 7.08E-02 2.73E-01

(0.33/1.31) (1.06E-02/1.54E+00) (9.04E-03/6.95E-01) (4.22E-02/1.84E+00)

3.3 4.82E-02 1.86E-02 7.15E-02

(1.31/5.31) (2.76E-03/4.04E-01) (2.37E-03/1.82E-01) (1.11E-02/4.82E-01)

12.9 9.17E-03 3.53E-03 1.36E-02

(5.31/20.41) (5.26E-04/7.70E-02) (4.51E-04/3.47E-02) (2.11E-03/9.18E-02)


23

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